On Long Range Dependence In Global Surface Temperature Series

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The Likelihood of Recent Record Warmth

model hemispheric and global mean surface temperatures are based on a combination ( TAS/TOS ) of surface air temperature ( TAS ) over land and sea surface temperature ( TOS ) over ocean. Here and elsewhere in the article, temperatures are defined as anomalies relative to the the long-term (1880-2014) mean.

Predicting the Response of Electricity Load to Climate Change

We paired the transmission zone load time-series with hourly surface air temperature time-series for the same years for sites selected from the updated 1991 2010 National Solar Resource Database (NSRDB). 2 2.1.1 Data filtering We created one filter prior to the regression analysis. Data pairs for which the temperature is in

Spatial and seasonal patterns in climate change, temperatures

correlation with average precipitation (r 0.78), but the long-term trend is similarly little affected. Even less connection of the SOI index and the temperature time series is evident (r 0.03). To further explore the relationship between trends in daily maximum temperature and the mean precipitation, a longitudinal

Spectral Characteristics of Instrumental and Climate Model

long-range memory model also implies the power-law form of the power spectral density S(f) } f2b.The power spectral density diverges as f / 0, but the spectral power for frequencies lower thanÐ f is f 0 S(f0)df0; f12b / 0asf / 0. The dominance of the low frequencies in the spectral density is a manifesta-tion of the long-range memory, and the


results for the dependence of global mean surface temperature and runoff on CO 2, for both glaciated and non-glaciated periods, coupled with new results for the temperature response to changes in solar radiation; (2) demonstration that values for the weathering-uplift factor f R (t) based on Sr isotopes as was done in GEOCARB II are

Limits of predictability: a global-scale investigation of

near-surface hourly temperature, dew point, relative humidity, sea level pressure, atmospheric wind speed, streamflow and precipitation. Through the use of the second-order climacogram (i.e., variance of the averaged process vs. scale) and climacospectrum (i.e., climacogram-based power-spectrum), we estimate the Long-Range-Dependence (LRD) for

Spatial Temporal Variability of Land Surface Temperature

ity of land surface temperature (LST) spatial distribution in the context of rapid urbanization, we introduced the multifractal detrended fluctuation analysis (MFDFA) to the LST patterns in XiamencityandXiamenIsland,China,during1994 2015.Results reveal the almost same long-range dependence of the LST spatial

Constraining Climate Sensitivity from the Seasonal Cycle in

tionship of the surface albedo feedback on seasonal and on decadal time scales for a series of AOGCMs. In this case, a strong dependence of the surface albedo and snow cover on temperature results in both a strong sea-sonal cycle and a strong global warming signal, in par-ticular in the high-latitude regions. This is in line with

Effects of Historical Urbanization in the Brussels Capital

the world database of land surface air temperatures and in the Global Climate Observing System (GCOS) sur-face network (Peterson et al. 1997). The temperature time series of Uccle has always been used as a rural sta-tion, whereasitisactuallyone ofthoseseriessuspected of being influenced by the urban heat island (UHI) created


ever, long-term data are not available. Therefore, the retroactive run is made with downward solar radiation from the global reanalysis (Kalnay et al., 1996). The forcing input data consists of precipitation, downward solar and longwave radiation, surface pressure, humidity, 2m temperature and horizontal windspeed, all interpolated to the 1/8

Global Sea Surface Temperature Analyses: Multiple Problems

Global Sea Surface Temperature Analyses: Multiple Problems and Their Implications for Climate Analysis, Modeling, and Reanalysis James W. Hurrell and Kevin E. Trenberth National Center for Atmospheric Research,* Boulder, Colorado *The National Center for Atmospheric Research is sponsored by the National Science Foundation.

On long range dependence in global surface temperature series

Abstract Long Range Dependence (LRD) scaling behavior has been argued to characterize long-term surface temperature time series. LRD is typically measured by the so-called Hurst coefficient, H Using synthetic temperature time series generated by a simple climate model with known physics, I demonstrate that the

Reconstructing Past Temperatures from Natural Proxies and

ally averaged temperature anomalies back to 1000AD, and explore the e ects of including external climate forcings within the recon-struction and of accounting for short-memory and long-memory fea-tures. Our reconstructions are based on two linear models, with the rst linking the latent temperature series to three main external forc-

Statistical estimation of global surface temperature response

long-range temporal dependence observed in global temperature variability. We have incorporated the model in a latent Gaussian modeling framework, which allows for the use of integrated nested Laplace approximations (INLAs) to perform full Bayesian analysis. As examples of applications, we estimate the GMST response to

Retrieval and Application of Land Surface Temperature

out the urgent need for long-term remote sensing based land surface skin temperature (LST) data in global warming studies to improve the limits of conventional 2-m World Meteorological Organization (WMO) surface air temperature observations. Currently, the long-term surface skin temperature dataset is only available over the ocean, i.e., sea

Multivariate and multiscale dependence in the global climate

variables including long-range spatial dependence, long-memory temporal processes, interactions at multiple scales, and nonlinearity of the underlying processes and relation-ships (Goddard et al. 2001; Hoerling et al. 2010). Complex networks have been motivated in climate to understand attributes of large-scale dynamics, for example, correla-

The slowdown in global mean surface temperature rise

all the RCP scenarios, with scenario dependence as shown in Table 2 below. To obtain the total change relative to 1850-1900, 0.61°C should be added to the change relative to 1986-20052. Table 2: Projected long-term changes in global mean surface temperature for 2081-2100. Scenario Change relative to 1986-2005


'Long-range dependence' models for the underlying spectra of hydro-climatic time series exhibiting a 1/f scaling have also been argued (e.g., Mandelbrot and Wallis, 1969; Bloomfield, 1992).

Satellite Oceanography: Sea-Surface Temperature and Climate

Temperature deficit Surface temperature range, -10 to 40oC N=13950 Change in BT with surface T relatively small for bands 22, 23 - significantly larger for bands 20, 31, 32 3.79µm 11.01µm 12.03µm 11.01µm 12.03µm 3.97µm 3.79µm 3.97µm 4.06µm 4.06µm

Chlorophyll -Temperature Time Series Project

as they make sense of the California Current Ecosystem. Students will analyze a temperature time-series from a 60-year record as a means of understanding that physical characteristics within a system govern biological responses. So, temperature data collected over space and time can reveal a pattern and act as a

Long -range memory in Earth surface temperatures: Spatial

is, the persistence of the time series. As a measure of linear dependence we have the autocovariancefunction: (s;t) = cov(x s;x t) = E[(x s )(x t )] (2.1) where is the mean value of x. By analyzing temperature measurements over a long time we can see that there exists dependence on almost all time scales, and that it is


also examined the sensitivity of analyzed global temperature to the chosen limit for station radius of influence (1200 km). The global mean temperature anomaly was insensitive to this choice for the range from 250 to 2000 km. The main effect is to make the global temperature anomaly map smoother as the radius of influence increases. However,

Spatial dependence of diurnal temperature range trends on

aggregated onto the 5 9 5 grid of the temperature data. For each grid box, only the primary PFT is considered based on the fractional cover of all PFTs within that box. 430 L. Zhou et al.: Spatial dependence of diurnal temperature range trends on precipitation from 1950 to 2004 123

Statistics of Regional Surface Temperatures after 1900: Long

1. (a) The black curve is the monthly temperature data for Moscow, Russia. (b) The blue curve is the monthly reconstructed temperature for the 58358 grid centered at 2.58S, 142.58W. (c) The red curve is the global mean temperature anomaly plotted with monthly resolution. (d) The PSD of the three time series in (a) (c). The

Sea Ice Albedo Feedback and Nonlinear Arctic Climate Change

This is roughly half the surface albedo difference between a typical sea ice cover and seawater. The balance expressed in (1) is depicted schematically in Plate 1. The steepness of the albedo ramp, the drop divided by the ramp temperature range, impacts the character of the nonlinearity. In particular, if the ramp is so steep that, as

Reconstructing Past Climate from Natural Proxies and

quency, with a parameter that also governs long memory. On account of the short calibration period, methodologically sound results from low frequency increasing-horizon asymptotics (seeTudor and Viens,2007) cannot be used to measure long-range dependence in our case, as there is simply not enough data.


teorological Organization (WMO) surface air tem-perature observations (T a). Currently, the long-term surface skin temperature dataset is only available over the ocean [i.e., sea surface temperature (SST), Bates and Diaz 1991]. Over land, developing such a dataset has proved more difficult due to the land s high sur-face heterogeneities.

Temperature Trend Analysis Using Non-Linear Regression of

2005), which goes to increase global-average surface temperatures by 2°C∼4.5°C by the end of this century. Other studies showed that there was an increase of 0.3 0.6 °C in the19 th century and it was raised 0.2 0.3

The global-scale temperature and moisture dependencies of

cells of global-scale models. In this study, we estimate the global-scale tem-perature and moisture dependencies of SOC decom-position by fitting the predictions of a simple, mechanistic decomposition model, implemented within a series of grid cells covering the terrestrial land surface, to the observed global-scale spatial distribution of SOC.

Natural Convection

The characteristics length is A/p where the surface area is A, and perimeter is p. a) Upper surface of a hot plate 1/3 7 11 1/ 4 4 7 0.15 10 10 0.54 10 10 Ra Ra Ra Ra Nu b) Lower surface of a hot plate Nu 0.27Ra1/ 4 105 Ra 1011 Example 1: isothermal vertical plate


GLOBAL OCEAN HEATING RATE Derivative of global heat content, from smoothed ocean heat content 1.0 0.5 0.0-0.5 G-2 1950 1960 1970 1980 1990 2000 2010 Surface to 700 m Schwartz, Surv. Geophys, 2012 Are fluctuations real? What is the uncertainty? Should do for individual reconstructions of ocean heat content to get sense of uncertainty.

How unusual is the recent series of warm years?

[4] We have analyzed global, regional and long station temperature records: global annual mean temperature in the period 1880 2006 from three global temperature data sets - Hadley Centre-CRU [Brohan et al., 2006], NASA GISS [Hansen et al., 2006] and NOAA NCDC [Smith and Reynolds, 2005]; the regional annual temperature means

Global surface temperature signals in pine ring-width

respectively, with global surface temperatures extracted from the Jones et al. [2001] variance-adjusted monthly surface temperature series. These data are from corrected observations in the form of a 5 5 global grid, and are not an interpolated data set. We used only grid cells with at least 50 years of data, and compared them with the tree ring


Both SST and land air temperature require adjust-ments to account for changes in, for example, depth or height of measurement, instrumentation, and siting. Improvement of estimated biases in historical measurements of SST will have a major effect on esti - mates of global surface temperature change and their uncertainty (Jones 2016).


FORECASTS OF GLOBAL SURFACE TEMPERATURE ANOMALIES Matt Newman University of Colorado/CIRES and NOAA/ESRL/PSD Newman, M., 2013: An empirical benchmark for decadal forecasts of global surface temperature anomalies. J. Climate, in press, doi 10.1175/JCLI-D-12-00590.1

Determination of Earth s transient and equilibrium climate

18 Relations among observed changes in global mean surface temperature, ocean heat content, ocean heating rate, and 19 calculated radiative forcing, all as a function of time over the twentieth century, that are based on a two-20 compartment energy balance model, are used to determine key properties of Earth's climate system. The increase in

The radio refractive index: its formula and refractivity data

2 Surface refractivity and height dependence 2.1 Refractivity as a function of height It has been found that the long-term mean dependence of the refractive index n upon the height h is well expressed by an exponential law: n(h) 1 N 0 10 6 exp ( h/h 0) (11) where: N 0

Measuring the surface temperatures of the earth from space

complementary to the near-surface air temperature ECV based on in situ measurements and reanalyses From a climate perspective, LST is important for: evaluation of land surface and land-atmosphere exchange processes constraint of surface energy budgets and flux variations global and regional observations of surface temperature variations

Nonlinear Trends, Long-Range Dependence, and Climate Noise

fluctuations. An analysis of the four temperature time series reveals evidence of long-range dependence (LRD) and nonlinear warming trends. The significance of these t rends is tested against climate noise. Three different methods are used to generate climate noise: (i) a short-r ange-dependent autoregressive process of first order

Impact of Vegetation Types on Surface Temperature Change

The impact of different surface vegetations on long-term surface temperature change is estimated by subtracting reanalysis trends in monthly surface temperature anomalies from observation trends over the last four decades. This is done using two reanalyses, namely, the 40-yr ECMWF (ERA-40) and NCEP